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Decaf

Decaf is a framework that implements convolutional neural networks, with the goal of being efficient and flexible. It allows one to easily construct a network in the form of an arbitrary Directed Acyclic Graph (DAG) and to perform end-to-end training.

For more usage check out the wiki. A great place to start is running ImageNet classification on an image.

For the pre-trained imagenet DeCAF feature and its analysis, please see our technical report on arXiv. Please consider citing our paper if you use Decaf in your research:

@article{donahue2013decaf,
  title={DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition},
  author={Donahue, Jeff and Jia, Yangqing and Vinyals, Oriol and Hoffman, Judy and Zhang, Ning and Tzeng, Eric and Darrell, Trevor},
  journal={arXiv preprint arXiv:1310.1531},
  year={2013}
}

For Anaconda users experiencing libm error: it is because anaconda ships with a libm.so binary that does not support GLIBC_2.15, which gets loaded earlier than the system libm. You can fix this error by you can replacing anaconda's libm file with a newer version.

About

Decaf is DEPRECATED! Please visit http://caffe.berkeleyvision.org/ for Caffe, the new framework that has all the good things: GPU computation, full train/test scripts, native C++, and an active community!

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